中国科技核心期刊

中文核心期刊

CSCD来源期刊

空间控制技术与应用 ›› 2020, Vol. 46 ›› Issue (6): 20-27.doi: 10.3969/j.issn.1674-1579.2020.06.003

• 论文与报告 • 上一篇    下一篇

一种小天体旋转参数估计方法

  

  • 出版日期:2020-12-25 发布日期:2021-01-19
  • 基金资助:
    国家自然科学基金资助项目(61671175)

A Method for Estimating Rotation Parameters of Small Body

  • Online:2020-12-25 Published:2021-01-19

摘要: 小天体旋转参数是科学数据,也是小天体测绘,着陆导航的基础数据.研究一种在小天体探测接近段过程中使用的基于图像上特征点跟踪和扩展卡尔曼滤波器的小天体旋转参数估计方法.该方法首先建立小天体旋转关系模型,表示小天体在相机坐标系中的姿态变化;然后定义小天体旋转的状态方程,推到了扩展卡尔曼滤波器的计算过程.通过对观测图像序列上的特征点跟踪,利用扩展卡尔曼滤波器方法得到小天体旋转轴指向和自转角速度估计.实验中,在仿真相机与小天体相机100 km距离上,分析了相机坐标系小天体坐标系之间的四元数初值,图像上特征点跟踪精度,相机的观测指向等因素对小天体旋转参数估计精度的影响.实验结果表明,提出的基于图像特征点跟踪和扩展卡尔曼滤波器的小天体旋转参数估计方法能够得到具有较高精度的估计结果.

关键词: 小天体探测, 旋转参数估计, 特征跟踪, 扩展卡尔曼滤波器

Abstract: The rotation parameters of a small body are scientific data, as well as basic data for small body mapping and landing navigation. A small body rotation parameter estimation method is studied based on image feature points tracking and extended Kalman filter in the process of small body exploration approach. The method first establishes a small body's rotation relationship model, which represents the attitude change of the small body in the camera coordinate system; then defines the state equation of the small body's rotation, and pushes to the calculation process of the extended Kalman filter. By tracking the feature points on the observation image sequence, the extended Kalman filter method is used to obtain the estimation of the rotation axis and the rotation angular velocity of the small body. In the experiments, when the distance between the simulated camera and the small body camera is 100km, the initial value of the quaternion between the camera coordinate system and the small body coordinate system, the tracking accuracy of the feature points on the image, and the camera's observation direction are analyzed for the rotation parameters of the small body. The experimental results show that the proposed method can obtain highprecision estimation results.

Key words: small body exploration, rotation parameter estimation, feature tracking, extended Kalman filter

中图分类号: 

  • V443+.5